Evaluation of a genetic risk score for severity of COVID-19 using human chromosomal-scale length variation.
Introduction.
The course of COVID-19 varies from asymptomatic to severe in patients. The basis for this range in symptoms is unknown. One possibility is that genetic variation is partly responsible for the highly variable response. We evaluated how well a genetic risk score based on chromosomal-scale length variation and machine learning classification algorithms could predict severity of response to SARS-CoV-2 infection.
Methods.
We compared 981 patients from the UK Biobank dataset who had a severe reaction to SARS-CoV-2 infection before 27 April 2020 to a similar number of age matched patients drawn for the general UK Biobank population. For each patient, we built a profile of 88 numbers characterizing the chromosomal-scale length variability of their germ line DNA. Each number represented one quarter of the 22 autosomes. We used the machine learning algorithm XGBoost to build a classifier that could predict whether a person would have a severe reaction to COVID-19 based only on their 88-number classification.
Results.
We found that the XGBoost classifier could differentiate between the two classes at a significant level (I as measured against a randomized control and (n as measured against the expected value of a random guessing algorithm (AUC=0.5). However, we found that the AUC of the classifier was only 0.51, too low for a clinically useful test.
Conclusion.
Genetics play a role in the severity of COVID-19, but we cannot yet develop a useful genetic test to predict severity.
Figure 1
Posted 23 Sep, 2020
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Evaluation of a genetic risk score for severity of COVID-19 using human chromosomal-scale length variation.
Posted 23 Sep, 2020
On 09 Oct, 2020
On 24 Sep, 2020
Received 24 Sep, 2020
On 24 Sep, 2020
On 22 Sep, 2020
Received 22 Sep, 2020
On 21 Sep, 2020
Invitations sent on 21 Sep, 2020
On 20 Sep, 2020
On 20 Sep, 2020
Received 30 Aug, 2020
On 30 Aug, 2020
Received 28 Jul, 2020
On 27 Jul, 2020
On 22 Jul, 2020
On 21 Jul, 2020
Invitations sent on 21 Jul, 2020
On 20 Jul, 2020
On 20 Jul, 2020
On 17 Jul, 2020
Introduction.
The course of COVID-19 varies from asymptomatic to severe in patients. The basis for this range in symptoms is unknown. One possibility is that genetic variation is partly responsible for the highly variable response. We evaluated how well a genetic risk score based on chromosomal-scale length variation and machine learning classification algorithms could predict severity of response to SARS-CoV-2 infection.
Methods.
We compared 981 patients from the UK Biobank dataset who had a severe reaction to SARS-CoV-2 infection before 27 April 2020 to a similar number of age matched patients drawn for the general UK Biobank population. For each patient, we built a profile of 88 numbers characterizing the chromosomal-scale length variability of their germ line DNA. Each number represented one quarter of the 22 autosomes. We used the machine learning algorithm XGBoost to build a classifier that could predict whether a person would have a severe reaction to COVID-19 based only on their 88-number classification.
Results.
We found that the XGBoost classifier could differentiate between the two classes at a significant level (I as measured against a randomized control and (n as measured against the expected value of a random guessing algorithm (AUC=0.5). However, we found that the AUC of the classifier was only 0.51, too low for a clinically useful test.
Conclusion.
Genetics play a role in the severity of COVID-19, but we cannot yet develop a useful genetic test to predict severity.
Figure 1